Background:The coronavirus disease 2019(COVID-19)outbreak has seriously endangered the health and lives of Chinese people.In this study,we predicted the COVID-19 epidemic trend and estimated the efficacy of several in...Background:The coronavirus disease 2019(COVID-19)outbreak has seriously endangered the health and lives of Chinese people.In this study,we predicted the COVID-19 epidemic trend and estimated the efficacy of several intervention strategies in the mainland of China.Methods:According to the COVID-19 epidemic status,we constructed a compartmental model.Based on reported data from the National Health Commission of People's Republic of China during January 10-February 17,2020,we estimated the model parameters.We then predicted the epidemic trend and transmission risk of COVID-19.Using a sensitivity analysis method,we estimated the efficacy of several intervention strategies.Results:The cumulative number of confirmed cases in the mainland of China will be 86763(95%CI:86067-87460)on May 2,2020.Up until March 15,2020,the case fatality rate increased to 6.42%(95%CI:6.16-6.68%).On February 23,2020,the existing confirmed cases reached its peak,with 60890 cases(95%CI:60350-61431).On January 23,2020,the effective reproduction number was 2.620(95%CI:2.567-2.676)and had dropped below 1.0 since February 5,2020.Due to governmental intervention,the total number of confirmed cases was reduced by 99.85%on May 2,2020.Had the isolation been relaxed from February 24,2020,there might have been a second peak of infection.However,relaxing the isolation after March 16,2020 greatly reduced the number of existing confirmed cases and deaths.The total number of confirmed cases and deaths would increase by 8.72 and 9.44%,respectively,due to a 1-day delayed diagnosis in non-isolated infected patients.Moreover,if the coverage of close contact tracing was increased to 100%,the cumulative number of confirmed cases would be decreased by 88.26%on May 2,2020.Conclusions:The quarantine measures adopted by the Chinese government since January 23,2020 were necessary and effective.Postponing the relaxation of isolation,early diagnosis,patient isolation,broad close-contact tracing,and strict monitoring of infected persons could effectively control the COVID展开更多
Severe acute respiratory syndrome (SARS) is a serious disease with many puzzling features. We present a simple, dynamic model to assess the epidemic potential of SARS and the effectiveness of control measures. With th...Severe acute respiratory syndrome (SARS) is a serious disease with many puzzling features. We present a simple, dynamic model to assess the epidemic potential of SARS and the effectiveness of control measures. With this model, we analysed the SARS epidemic data in Beijing. The data fitting gives the basic case reproduction number of 2.16 leading to the outbreak, and the variation of the effec-tive reproduction number reflecting the control effect. No-ticeably, our study shows that the response time and the strength of control measures have significant effects on the scale of the outbreak and the lasting time of the epidemic.展开更多
In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span>&...In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span><span style="font-family:Verdana;">al copies of a Susceptible-Exposed-Infectious-Recovered (<i></span><i><span style="font-family:Verdana;">SEIR</span></i><span style="font-family:Verdana;"></i>) compart</span></span><span style="font-family:Verdana;">mental model, and compare it to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number.</span><span style="font-family:Verdana;"> We also discuss the limitations and possible extensions of the employed model.展开更多
The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic inf...The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R<sub>0</sub>, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.展开更多
Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,generated from an ensemble of models,have been informing public health policy throughout the COVID-19 pand...Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,generated from an ensemble of models,have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland(UK).However,estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the“emergency”to“endemic”phase of the pandemic.The Office for National Statistics(ONS)COVID-19 Infection Survey(CIS)provided an opportunity to continue estimating these parameters in the absence of other data streams.We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time.The resulting fitted curve was used to estimate the“ONS-based”and across the four nations of the UK.Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters.Depending on the nation and parameter,we found that up to 77%of the variance in the government-published estimates can be explained by the ONS-based estimates,demonstrating the value of this singular data stream to track the epidemic in each of the four nations.We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates.Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations,further underlining the enormous value of such population-level studies of infection.This is not intended as an alternative to ensemble modelling,rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.展开更多
Control measures during the coronavirus disease 2019(COVID-19)outbreak may have limited the spread of infectious diseases.This study aimed to analyze the impact of COVID-19 on the spread of hand,foot,and mouth disease...Control measures during the coronavirus disease 2019(COVID-19)outbreak may have limited the spread of infectious diseases.This study aimed to analyze the impact of COVID-19 on the spread of hand,foot,and mouth disease(HFMD)in China.A mathematical model was established to fit the reported data of HFMD in six selected cities in China's Mainland from 2015 to 2020.The absolute difference(AD)and relative difference(RD)between the reported incidence in 2020,and simulated maximum,minimum,or median incidence of HFMD in 2015-2019 were calculated.The incidence and R effof HFMD have decreased in six selected cities since the outbreak of COVID-19,and in the second half of 2020,the incidence and R effof HFMD have rebounded.The results show that the total attack rate(TAR)in 2020 was lower than the maximum,minimum,and median TAR fitted in previous years in six selected cities(except Changsha City).For the maximum,median,minimum fitted TAR,the range of RD(%)is 42·20-99·20%,36·35-98·41%48·35-96·23%(except Changsha City)respectively.The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission.展开更多
We develop a mathematical model to investigate the effect of contact tracing on containing epidemic outbreaks and slowing down the spread of transmissible diseases.We propose a discrete-time epidemic model structured ...We develop a mathematical model to investigate the effect of contact tracing on containing epidemic outbreaks and slowing down the spread of transmissible diseases.We propose a discrete-time epidemic model structured by disease-age which includes general features of contact tracing.The model is fitted to data reported for the early spread of COVID-19 in South Korea,Brazil,and Venezuela.The calibrated values for the contact tracing parameters reflect the order pattern observed in its performance intensity within the three countries.Using the fitted values,we estimate the effective reproduction number R_(e)and investigate its responses to varied control scenarios of contact tracing.Alongside the positivity of solutions,and a stability analysis of the disease-free equilibrium are provided.展开更多
It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we deve...It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-remov ed(SEAIQR)model with stage-specific interventions based on recent Shanghai epidemic data,considering a large number of asymptomatic infectious,the changing parameters,and control procedures.The data collected from March 1st,2022 to April 15th,2022 were used to fit the model,and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting.We then calculated the effective regeneration number(Rt)and analyzed the sensitivity of different measures scenarios.Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai,and Pudong is the district with the most positive cases.The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th,2022,with 1963 and 28,502 cases,respectively,and zero community transmission may be achieved in early to mid-May.The prediction errors for newly confirmed cases were considered to be reasonable,and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th.The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477~47,749 and 402,254~730,176,respectively.At the beginning of the outbreak,Rt was 6.69.Since the implementation of comprehensive control,Rt showed a gradual downward trend,dropping to below 1.0 on April 15th,2022.With the early implementation of control measures and the improvement of quarantine rate,recovery rate,and immunity threshold,the peak number of infections will continue to decrease,whereas the earlier the control is implemented,the earlier the turning point of the epidemic will arrive.The proposed time-depen展开更多
基金The study was supported by grants from the Fundamental Research Funds for the Central Universities for COVID-19(xzy032020040,xzy032020027,xzy032020026,xzy012019107)Zhejiang University special scientific research fund for COVID-19 prevention and control(2020XGZX056)+2 种基金National Natural Science Foundation of China(11971375,11571272,11631012 and 11801435)National Science and Technology Major Project of China(2018ZX10721202)Natural Science Foundation of Shaanxi Province(2019JM-273,2019JQ-187).
文摘Background:The coronavirus disease 2019(COVID-19)outbreak has seriously endangered the health and lives of Chinese people.In this study,we predicted the COVID-19 epidemic trend and estimated the efficacy of several intervention strategies in the mainland of China.Methods:According to the COVID-19 epidemic status,we constructed a compartmental model.Based on reported data from the National Health Commission of People's Republic of China during January 10-February 17,2020,we estimated the model parameters.We then predicted the epidemic trend and transmission risk of COVID-19.Using a sensitivity analysis method,we estimated the efficacy of several intervention strategies.Results:The cumulative number of confirmed cases in the mainland of China will be 86763(95%CI:86067-87460)on May 2,2020.Up until March 15,2020,the case fatality rate increased to 6.42%(95%CI:6.16-6.68%).On February 23,2020,the existing confirmed cases reached its peak,with 60890 cases(95%CI:60350-61431).On January 23,2020,the effective reproduction number was 2.620(95%CI:2.567-2.676)and had dropped below 1.0 since February 5,2020.Due to governmental intervention,the total number of confirmed cases was reduced by 99.85%on May 2,2020.Had the isolation been relaxed from February 24,2020,there might have been a second peak of infection.However,relaxing the isolation after March 16,2020 greatly reduced the number of existing confirmed cases and deaths.The total number of confirmed cases and deaths would increase by 8.72 and 9.44%,respectively,due to a 1-day delayed diagnosis in non-isolated infected patients.Moreover,if the coverage of close contact tracing was increased to 100%,the cumulative number of confirmed cases would be decreased by 88.26%on May 2,2020.Conclusions:The quarantine measures adopted by the Chinese government since January 23,2020 were necessary and effective.Postponing the relaxation of isolation,early diagnosis,patient isolation,broad close-contact tracing,and strict monitoring of infected persons could effectively control the COVID
基金supported by the Major State Research Project(Grant No.G2000077305)the National Natural Science Foundation of China(NSFC)and Chinese Academy of Sciences
文摘Severe acute respiratory syndrome (SARS) is a serious disease with many puzzling features. We present a simple, dynamic model to assess the epidemic potential of SARS and the effectiveness of control measures. With this model, we analysed the SARS epidemic data in Beijing. The data fitting gives the basic case reproduction number of 2.16 leading to the outbreak, and the variation of the effec-tive reproduction number reflecting the control effect. No-ticeably, our study shows that the response time and the strength of control measures have significant effects on the scale of the outbreak and the lasting time of the epidemic.
文摘In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span><span style="font-family:Verdana;">al copies of a Susceptible-Exposed-Infectious-Recovered (<i></span><i><span style="font-family:Verdana;">SEIR</span></i><span style="font-family:Verdana;"></i>) compart</span></span><span style="font-family:Verdana;">mental model, and compare it to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number.</span><span style="font-family:Verdana;"> We also discuss the limitations and possible extensions of the employed model.
文摘The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R<sub>0</sub>, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.
基金This work was supported by the NIHR HPRU in Emerging and Zoonotic Infections,a partnership between the United Kingdom Health Security Agency(UKHSA),University of Oxford,University of Liverpool and Liverpool School of Tropical Medicine[grant number NIHR200907 supporting RM and CAD]the MRC Centre for Global Infectious Disease Analysis[grant number MR/X020258/1],funded by the UK Medical Research Council(MRC)This UK funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking.RM was also supported by the UKHSA and the Isaac Newton Institute(INI)Knowledge Transfer Network(KTN)in funding and coordinating a 3-month placement at the UK Health Security Agency,respectively.
文摘Key epidemiological parameters,including the effective reproduction number,,and the instantaneous growth rate,,generated from an ensemble of models,have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland(UK).However,estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the“emergency”to“endemic”phase of the pandemic.The Office for National Statistics(ONS)COVID-19 Infection Survey(CIS)provided an opportunity to continue estimating these parameters in the absence of other data streams.We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time.The resulting fitted curve was used to estimate the“ONS-based”and across the four nations of the UK.Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters.Depending on the nation and parameter,we found that up to 77%of the variance in the government-published estimates can be explained by the ONS-based estimates,demonstrating the value of this singular data stream to track the epidemic in each of the four nations.We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates.Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations,further underlining the enormous value of such population-level studies of infection.This is not intended as an alternative to ensemble modelling,rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.
基金This study was partly supported by the Bill&Melinda Gates Foun-dation(INV-005834).
文摘Control measures during the coronavirus disease 2019(COVID-19)outbreak may have limited the spread of infectious diseases.This study aimed to analyze the impact of COVID-19 on the spread of hand,foot,and mouth disease(HFMD)in China.A mathematical model was established to fit the reported data of HFMD in six selected cities in China's Mainland from 2015 to 2020.The absolute difference(AD)and relative difference(RD)between the reported incidence in 2020,and simulated maximum,minimum,or median incidence of HFMD in 2015-2019 were calculated.The incidence and R effof HFMD have decreased in six selected cities since the outbreak of COVID-19,and in the second half of 2020,the incidence and R effof HFMD have rebounded.The results show that the total attack rate(TAR)in 2020 was lower than the maximum,minimum,and median TAR fitted in previous years in six selected cities(except Changsha City).For the maximum,median,minimum fitted TAR,the range of RD(%)is 42·20-99·20%,36·35-98·41%48·35-96·23%(except Changsha City)respectively.The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission.
文摘We develop a mathematical model to investigate the effect of contact tracing on containing epidemic outbreaks and slowing down the spread of transmissible diseases.We propose a discrete-time epidemic model structured by disease-age which includes general features of contact tracing.The model is fitted to data reported for the early spread of COVID-19 in South Korea,Brazil,and Venezuela.The calibrated values for the contact tracing parameters reflect the order pattern observed in its performance intensity within the three countries.Using the fitted values,we estimate the effective reproduction number R_(e)and investigate its responses to varied control scenarios of contact tracing.Alongside the positivity of solutions,and a stability analysis of the disease-free equilibrium are provided.
基金This study was supported by the National Key Research and Development Program of China(2021YFC2301603).
文摘It’s urgently needed to assess the COVID-19 epidemic under the“dynamic zero-COVID policy”in China,which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control.Here,we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-remov ed(SEAIQR)model with stage-specific interventions based on recent Shanghai epidemic data,considering a large number of asymptomatic infectious,the changing parameters,and control procedures.The data collected from March 1st,2022 to April 15th,2022 were used to fit the model,and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting.We then calculated the effective regeneration number(Rt)and analyzed the sensitivity of different measures scenarios.Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai,and Pudong is the district with the most positive cases.The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th,2022,with 1963 and 28,502 cases,respectively,and zero community transmission may be achieved in early to mid-May.The prediction errors for newly confirmed cases were considered to be reasonable,and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th.The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477~47,749 and 402,254~730,176,respectively.At the beginning of the outbreak,Rt was 6.69.Since the implementation of comprehensive control,Rt showed a gradual downward trend,dropping to below 1.0 on April 15th,2022.With the early implementation of control measures and the improvement of quarantine rate,recovery rate,and immunity threshold,the peak number of infections will continue to decrease,whereas the earlier the control is implemented,the earlier the turning point of the epidemic will arrive.The proposed time-depen
